Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 46 for “"Finite Mixture"”.
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A finite mixture approach for household residential choices
… the typing of households by implementing a finite mixture model which gives a probability distribution of an individual household being a particular type. The model best fitted the array of households into two types. The types exhibited differences in their attitudinal and demographic …
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Probabilistic Finite Mixture Clustering of Genetic Expression Microarray Data
Exploratory cluster analysis of large data sets often implements k-means or hierarchical methods. These routines typically exhibit limitations which reduces the reliability of the results. Both assign observations to the one component for which its Euclidean distance from the center is smallest. …
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Finite Mixture Model Specifications Accommodating Treatment Nonresponse in Experimental Research
… multiple signals from data streams with Gaussian mixture models, where their use is well matched to accommodate researchers in this predicament. While the mathematics underpinning models in either application remains unchanged, there are stark differences. In signal processing, results are …
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Bayesian analysis of finite mixture distributions using the allocation sampler
Finite mixture distributions are receiving more and more attention from statisticians in many different fields of research because they are a very flexible class of models. They are typically used for density estimation or to model population heterogeneity. One can think of a finite mixture …
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A Finite Mixture Approach to Covariance Structure Modeling With Unknown, Heterogeneous Populations
… population membership is unknown. The field of finite mixture analysis, however, addresses the issues of unknown heterogeneous populations. The finite mixture methodology, therefore, is applied to the problem of covariance structure modeling when the sample represents an unknown mixture of …
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Application of Hidden Markov Model in Finite Mixture Modeling of High-Dimensional Data
Finite mixture models (FMMs) are widely used in practice and are famous for modeling heterogeneous data in a convenient and effective way. Owing to their flexibility, finitemixtures have since been applied to a wide range of problems in diverse fields, includingimage analysis, medicine, …
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MULTIVARIATE FINITE MIXTURE GROUP-BASED TRAJECTORY MODELING WITH APPLICATION TO MENTAL HEALTH STUDIES
… modeling (GBTM). GBTM was an extension of the finite mixture modeling (FMM) method that has been widely used in various fields of trajectory analysis in the last 25 years (Nagin & Odgers, 2010). GBTM was able to detect unobserved subgroups based on the multinomial logit function (Nagin, 1999). …
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Supervised Classification Using Copula and Mixture Copula
… In fact for some data, the pattern vector is a mixture of discrete and continuous random variables. In this dissertation, we use copula densities to model class conditional distributions. Such types of densities are useful when the marginal densities of a pattern vector are not normally …
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Modelling breakdown durations in simulation models of engine assembly lines
… the similarity of two sets of data. We use finite mixture distributions fitted to the breakdown durations data of groups of machines as the input models for the simulation models. We evaluate the complete modelling methodology that involves the use of the Arrows classification method and …
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Methodological Development with Machine Learning and Bayesian Approaches in Cancer and Nutrition Research
… nutrition research. Chapter 2 explores Bayesian finite mixture models for adherence estimation and clinical trial design aimed at improving adherence. Bayesian methods use Bayes' theorem to update knowledge about parameters in a statistical model with new observed data, providing a probabilistic …
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Advances in mixture modeling and model based clustering
… is model-based which relies on the idea of finite mixture models. This dissertations will propose new advances in clustering area mostly related to model-based clustering and its extension to the K-means algorithm. This report has five chapters. The first chapter is a literature review on …
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Online reviews and consumers' willingness to pay: the role of uncertainty
… Due to the nature of the transactional data, a finite mixture model is used to estimate the weighting function, and hypotheses are tested at the group instead of the individual level. A simulation study demonstrates the validity of using a finite mixture model to estimate the weighting function …
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Multiple-Species Models for True Abundances Allowing for Heterogeneity of Capture Between and Within Species
… this fact. In this paper, we explore the use of finite mixture modeling to quantify species' true abundances within a population while allowing for unknown differences in the individuals' capture probabilities. This modeling also allows us to develop diversity and evenness measures based on true …
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Bayesian statistics for fishery stock assessment and management
… The importance function is given as a finite mixture of heavy-tailed Student distributions. The performance of the method is tested in five case-studies, two of which use data simulation. Real data refer to Skeena river salmon (Oncorhynchus nerka), Orange Roughy (Hoplostethus …
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Robust mixture regression using mean-shift penalisation
The purpose of finite mixture regression (FMR) is to model the relationship between a response and feature variables in the presence of latent groups in the population. The different regression structures are quantified by the unique parameters of each latent group. The Gaussian mixture regression …
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Least squares mixture decomposition estimation
The Least Squares Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized …
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Bayesian Approach Dealing with Mixture Model Problems
… we focus on two research topics related to mixture models. The first topic is Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite …
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Advanced methods for analysing and modelling multivariate palaeoclimatic time series
… transportation as well as deposition mechanisms. Finite mixture models may be used to approximate the corresponding distribution functions appropriately. In order to give a complete description of the statistical uncertainty of the parameter estimates in such models, the concept of asymptotic …
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The Generalized Linear Mixed Model for Finite Normal Mixtures with Application to Tendon Fibrilogenesis Data
We propose the generalized linear mixed model for finite normal mixtures (GLMFM), as well as the estimation procedures for the GLMFM model, which are widely applicable to the hierarchical dataset with small number of individual units and multi-modal distributions at the lowest level of clustering. …
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Bayesian analysis for mixtures of discrete distributions with a non-parametric component
Bayesian finite mixture modelling is a flexible parametric modelling approach for classification and density fitting. Many application areas require distinguishing a signal from a noise component. In practice, it is often difficult to justify a specific distribution for the signal component, …
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